Previously fellow Shashaank Vattikuti and I analyzed the entire GWAS SNP marker set simultaneously with respect to the genetic contribution to obesity and type II diabetes traits. We used a mixed-effects linear model of all the SNPs to estimate the heritability of a set of traits. In addition, we also estimated the genetic contribution that is shared between traits. We showed that approximately half of the known heritability estimated using classical methods are captured collectively by the common SNPs. Previously, only a small fraction of the heritability could be explained from sets of single SNPs, which led to what has been called the problem of missing heritability. Our work showed that the heritability is not missing but merely hidden in the noise. We also showed that the heritability estimated by the SNPs increases with the number of SNPs, which also indicates that the genetic information may be spread over large segments of the genome. We are continuing the work by validating in other data sets and to look for more specific large scale patterns of in the markers for each phenotype in these data sets as was specified in our original request. With Stephen Hsu and fellows Shashaank Vattikuti and James Lee, we are currently studying how the number of genetic differences between pairs of people over the entire genome changes with their average phenotypic value and use theoretical results to interpret the pattern in light of the underlying genetic architecture. In our initial research we use height as the focal phenotype. Height is a classical object of study in human genetics and is of interest in its own right as a correlate of diseases such as cancer and type 2 diabetes. We will then apply this methodological work to the genetic basis of several traits. We also applied the statistical theory of compressed sensing to analyzing GWAS data.